Performed statistical-EDA and normalization analysis on digitized mass images with 10 nuclei features (radius, texture)
Predicted malignant - benign cancer using Logistic, LDA-QDA, KNN, Lasso-Ridge classifiers with 0.89, 0.88, 0.92, 0.96 and 0.97 accuracies respectively along with decision boundaries and ROC curves
Source Information
a) Creators of data:
Dr. William H. Wolberg, General Surgery Dept., University of Wisconsin, Clinical Sciences Center, Madison, WI 53792 wolberg@eagle.surgery.wisc.edu
W. Nick Street, Computer Sciences Dept., University of Wisconsin, 1210 West Dayton St., Madison, WI 53706 street@cs.wisc.edu
Olvi L. Mangasarian, Computer Sciences Dept., University of Wisconsin, 1210 West Dayton St., Madison, WI 53706 olvi@cs.wisc.edu
Relevant information
Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at http://www.cs.wisc.edu/~street/images/